We propose a method called Rule-based ESP (RESP) for utilizing prior
knowledge in evolving Artificial Neural Networks (ANNs). First,
KBANN-like techniques are used to transform a set of rules into an ANN,
then the ANN is trained using the Enforced Subpopulations (ESP)
neuroevolution method. Empirical results in the Prey Capture domain show
that RESP can reach higher level of performance than ESP. The results also
suggest that incremental learning is not necessary with RESP, and it is
often easier to design a set of rules than an incremental evolution
scheme. In addition, an experiment with some of the rules deleted
suggests that RESP is robust even with an incomplete knowledge base. RESP
therefore provides a robust methodology for scaling up neuroevolution to
harder tasks by utilizing existing knowledge about the domain.